Introduction
The sudden spike in false positives from Exabeam's User and Entity Behavior Analytics (UEBA) engine yesterday presents a critical issue that demands immediate attention and thorough analysis. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term implications for our product.
My analysis will follow a structured framework, beginning with clarifying questions to gather essential context, followed by a comprehensive examination of potential causes, data analysis, hypothesis formation, and finally, a proposed resolution plan.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minute)
Why it matters: Helps pinpoint the timeframe for investigation. Expected answer: Yes, within the last 24 hours. Impact on approach: Narrow focus to recent changes or events.
Why it matters: Indicates severity and potential scope of the problem. Expected answer: A significant increase, likely over 50%. Impact on approach: Influences urgency and breadth of investigation.
Why it matters: Could directly impact false positive rates. Expected answer: No recent changes to models. Impact on approach: If yes, focus on model adjustments; if no, look elsewhere.
Why it matters: Helps identify if it's a global issue or segment-specific. Expected answer: Concentrated in certain user segments. Impact on approach: Target investigation on affected segments if applicable.
Why it matters: Changes in input data could affect analysis accuracy. Expected answer: Some recent updates to data integrations. Impact on approach: Investigate recent data source changes if confirmed.
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